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本文提出一种基于物理信息神经网络的量子绝热捷径方案.与传统的绝热捷径技术相比，创新性的引入机器学习技术，利用参数化的物理信息神经网络解含参数的微分方程，将神经网络作为量子绝热演化过程的逼近函数，并将含参数的微分方程和微分方程的各种物理约束条件作为参数化的神经网络的损失函数，训练神经网络，拟合量子系统演化过程，获得布居反转的驱动控制函数.数值实验表明，量子系统可以在短时间内实现布居反转，并且具有很高的保真度、很强的鲁棒性.神经网络具有很强的计算能力，适合复杂系统的驱动控制函数的生成.与传统的绝热捷径技术相比，具有更好的效果和更强的实用性.This paper proposes a quantum shortcuts to adiabaticity scheme based on physics-informed neural networks. Compared with traditional shortcuts to adiabaticity techniques, our approach innovatively integrates machine learning methodologies by employing parameterized physics-informed neural networks to solve parameterized differential equations. The neural networks serves as an approximating function for quantum adiabatic evolution processes, while incorporating parameter-dependent differential equations and various physical constraints as components of the loss function. Through networks training, we effectively simulate quantum system dynamics and derive driving control fields for population inversion. Numerical simulations demonstrate that the quantum system can achieve rapid population inversion within significantly reduced time while maintaining high fidelity and exceptional robustness against parameter fluctuations. The neural networks exhibit remarkable computational capabilities, particularly suitable for generating control functions in complex quantum systems. Compared with conventional counter-diabatic driving and transitionless quantum driving methods, this PINN-based framework not only achieves better control performance but also offers improved practicality for experimental implementations. The success of this methodology suggests promising applications in quantum control tasks including but not limited to quantum state preparation, quantum gate optimization, and adiabatic quantum computing acceleration.
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Keywords:
- shortcuts to adiabaticity /
- deep learning /
- physics-Informed neural networks /
- differential equation
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